Gke Golden Path

by google55b4e13eba6dNo licenseListed Oct 8, 2026Updated Oct 8, 2026

Provides GKE golden path configuration defaults, production readiness checklists, and cluster default patterns. Use when designing GKE clusters, verifying GKE production readiness, or checking configurations against GKE defaults. Don't use for setting up workload autoscaling specifically (use gke-workload-scaling instead).

FeaturedInstructions onlyDevOps & Cloud
AI-generated overview

Provides GKE golden path cluster defaults, production readiness checklists, and deviation guidance for designing or auditing GKE clusters.

What it does
Supplies recommended Autopilot configuration defaults for production GKE clusters, including always-apply settings, customer-configurable settings with trade-offs, and Day-0 versus Day-1 decision guidance. It also provides production readiness checks, upgrade disruption guidance, and a reference cluster policy YAML asset. When auditing existing clusters, it compares them against the golden path and reports deviations with severity and remediation.
When to use it
Use when designing GKE clusters, verifying GKE production readiness, or checking existing cluster configurations against GKE defaults. It is not intended for setting up workload autoscaling specifically, which is covered by separate skills.
Requirements
Requires access to GKE MCP tools (get_cluster, create_cluster, update_cluster) or gcloud and kubectl for cluster operations. Project ID, region, and cluster name are required inputs. Ships a reference YAML asset but no scripts.

GKE Golden Path Configuration

The golden path is the recommended Autopilot configuration for production clusters. It defines sensible defaults — when the user requests different settings, apply them and note relevant trade-offs. For setting up autoscaling specifically, use gke-cluster-autoscaler for node autoscaling or gke-workload-scaling for workload autoscaling (HPA/VPA).

MCP Tools: get_cluster, create_cluster, update_cluster

Rules

  1. Default to the golden path. Use golden path values unless the user requests otherwise. When deviating, note trade-offs but respect the user's choice.
  2. Day-0 vs Day-1. Flag Day-0 decisions (networking, private nodes, subnets, IP allocation) prominently — they are hard/impossible to change after creation.
  3. Tool preference: MCP > gcloud > kubectl. MCP is preferred as it directly interfaces with GKE APIs with structured data, reducing shell syntax errors and parsing ambiguities. See the gke-basics skill's CLI reference for full coverage matrix and override options. If the user says "use gcloud" or "use kubectl", respect that for the session.
  4. Document decisions and rationale, especially for Day-0 choices and golden path deviations.

Required Inputs

If the user is unsure, use golden path defaults.

  • Project ID (required)
  • Region (required, e.g., us-central1)
  • Cluster name (required)
  • Environment type: dev/test or production (defaults to production)
  • Networking: bring-your-own VPC/subnet or auto-create (default: auto-create)
  • Scale expectations: expected node/pod count, workload types
  • Cost constraints: Spot VM tolerance, budget considerations

Always-Apply Defaults

Recommended best practices applied by default. If the user requests a different setting, apply it and briefly note the security or operational trade-off.

SettingGolden Path Value
autopilot.enabledtrue
privateClusterConfig.enablePrivateNodestrue
masterAuthorizedNetworksConfig.privateEndpointEnforcementEnabledtrue
secretManagerConfig.enabled + rotationInterval: 120strue
rbacBindingConfig.enableInsecureBinding*false (both)
workloadIdentityConfig.workloadPoolenabled
networkConfig.datapathProviderADVANCED_DATAPATH
networkConfig.dnsConfig.clusterDnsCLOUD_DNS
autoscaling.autoscalingProfileOPTIMIZE_UTILIZATION
verticalPodAutoscaling.enabledtrue
monitoringConfig componentsSYSTEM_COMPONENTS, STORAGE, POD, DEPLOYMENT, STATEFULSET, DAEMONSET, HPA, JOBSET, CADVISOR, KUBELET, DCGM, APISERVER, SCHEDULER, CONTROLLER_MANAGER
loggingConfig componentsSYSTEM_COMPONENTS, WORKLOADS (enabled by default)
advancedDatapathObservabilityConfig.enableMetricstrue
nodeConfig.shieldedInstanceConfig.enableSecureBoottrue
nodeConfig.workloadMetadataConfig.modeGKE_METADATA
nodeConfig.gcfsConfig.enabled / gvnic.enabledtrue / true
addonsConfig.statefulHaConfig.enabledtrue
Storage CSI drivers (Filestore, GCS FUSE, Parallelstore)enabled
Pod Security Standardsrestricted on production namespaces

Customer-Configurable Settings

These have golden path defaults but customers may deviate with valid justification. Ask before changing.

SettingDefaultWhy Deviate
dnsEndpointConfig.allowExternalTraffictrueRestrict if cluster only accessed from within VPC
autoIpamConfig / createSubnetworktrue / trueCustomer has pre-existing VPC/subnets
maxPodsPerNode48 (this golden path's choice)Halves per-node IP consumption (/25 instead of /24). Not a GKE default (Standard defaults to 110, Autopilot to 32); raise for high pod-density at the cost of more CIDR space
subnetworkauto-createdCustomer brings existing subnets
Release channel + maintenance windowsREGULAR channel with a recurring maintenance windowAdd targeted maintenance exclusions (keep under ~6 months) only for critical freezes — see the gke-upgrades skill
nodeConfig.bootDisk.diskTypepd-balancedpd-ssd for I/O-intensive, pd-standard for cost

Note: Autopilot selects node machine types automatically (e.g., ek-standard-8 may appear in describe output); the machine type is not customer-configurable in Autopilot. Steer workload placement via ComputeClasses instead.

Guardrails

  • Do not request or output secrets (tokens, keys, service account JSON).
  • Resolve project/cluster context from the conversation, MCP tools, or gcloud config get-value project; ask the user only if it cannot be resolved.
  • For Day-0 decisions, always ask clarifying questions before proceeding.
  • For Day-1 features, propose golden path defaults with trade-offs and let the customer confirm.
  • Do not promise zero downtime — see Upgrade Disruption below for what to advise instead.
  • When auditing existing clusters, compare against golden path and report deviations with severity and remediation.

Upgrade Disruption

Never promise zero downtime for node upgrades, on any configuration. Node upgrades cordon and drain nodes, which evicts Pods. Draining honors PodDisruptionBudgets and terminationGracePeriodSeconds for up to one hour, after which GKE forcefully evicts the remaining Pods so the upgrade can proceed. A PDB narrows the window; it cannot veto the upgrade. Say so plainly rather than implying the disruption can be eliminated.

What to recommend, all four — not a subset:

  • PodDisruptionBudgets with minAvailable set so eviction cannot take the last healthy replica. A PDB that can never be satisfied stalls the drain for an hour and then loses anyway.

  • At least 2 replicas, spread across zones with topology spread constraints. A single-replica Deployment has downtime by definition.

  • Readiness probes that reflect real serving health, so traffic drains before the Pod dies.

  • Surge upgrade settings on the node pool. Surge is the default strategy; the default is maxSurge=1, maxUnavailable=0 — one extra node is created and made ready before an old one is drained.

    SettingControlsDefault
    maxSurgeAdditional nodes added per zone during the upgrade1
    maxUnavailableNodes simultaneously unavailable per zone0

    Nodes upgraded at once is the sum of the two, capped at 20 (Autopilot) and 100 (Standard). Multi-zone node pools upgrade one zone at a time. Raising maxUnavailable trades availability for speed; raising maxSurge trades cost for availability.

Caveat: externalTrafficPolicy: Local does not work with parallel node drains, so it constrains aggressive surge configurations.

For rollback procedures and maintenance windows, see the gke-upgrades skill.

Golden Path Config

See golden-path-autopilot.yaml for the full cluster-level policy settings.

Source and attribution

Source:google/skillsinskills/cloud/gke-golden-pathat commit55b4e13

License: No license

Content belongs to its original authors. SourceWeft indexes it from a public repository.

Report or request removal

More from google/skills

Dpop Adoption

google

Featured

Guides implementation of OAuth 2.0 DPoP (RFC 9449) sender-constrained refresh tokens for Google's OAuth platform.

SecurityOct 8, 2026

Finding Google Skills

google

Featured

Google platform decision and setup guidance, loaded on demand from Google's skill catalog. Use when a developer is choosing or setting up part of their stack, such as where to run a service, a database, storage, messaging, authentication, analytics, ads, or AI model serving, and a Google product is a reasonable candidate - whether or not a vendor is named - or when a request names a Google product or API. Brings in the matching Google skill so the answer can weigh Google options, their trade-offs, and when they are not the right fit. Skip when the stack is already settled on another provider and no Google product is named, or the task involves no platform choice.

Awaiting classificationOct 8, 2026

Spanner Basics

google

Featured

Guides Google Cloud Spanner administration, schema design, querying and performance diagnosis.

Data & AnalyticsOct 8, 2026

Secops Triage

google

Featured

Guides SOC analysts through triaging Google SecOps security alerts, from investigation to closure or escalation.

SecurityOct 8, 2026

Secops Investigate

google

Featured

Guides SOC analysts through deep security incident and entity investigations in Google SecOps using UDM queries and timelines.

SecurityOct 8, 2026

Secops Hunt

google

Featured

Guides proactive threat hunting in Google SecOps using UDM queries, IoC lookback, prevalence and outlier analysis.

SecurityOct 8, 2026